{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T05:07:31Z","timestamp":1777266451837,"version":"3.51.4"},"reference-count":87,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2023,4,5]],"date-time":"2023-04-05T00:00:00Z","timestamp":1680652800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,4,5]],"date-time":"2023-04-05T00:00:00Z","timestamp":1680652800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["DDG-2020-00034"],"award-info":[{"award-number":["DDG-2020-00034"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Machine Vision and Applications"],"published-print":{"date-parts":[[2023,5]]},"DOI":"10.1007\/s00138-023-01390-6","type":"journal-article","created":{"date-parts":[[2023,4,5]],"date-time":"2023-04-05T17:02:06Z","timestamp":1680714126000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":60,"title":["Interpretable visual transmission lines inspections using pseudo-prototypical part network"],"prefix":"10.1007","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0819-8221","authenticated-orcid":false,"given":"Gurmail","family":"Singh","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3723-616X","authenticated-orcid":false,"given":"Stefano Frizzo","family":"Stefenon","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8610-661X","authenticated-orcid":false,"given":"Kin-Choong","family":"Yow","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,4,5]]},"reference":[{"key":"1390_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijepes.2021.107379","volume":"134","author":"FM Aboshady","year":"2022","unstructured":"Aboshady, F.M.: Modified distance protection for transmission line with hexagonal phase-shifting transformer. Int. J. Electr. Power Energy Syst. 134, 107379 (2022). https:\/\/doi.org\/10.1016\/j.ijepes.2021.107379","journal-title":"Int. J. Electr. Power Energy Syst."},{"issue":"8","key":"1390_CR2","doi-asserted-by":"publisher","first-page":"5345","DOI":"10.1109\/TIM.2020.2965635","volume":"69","author":"B Wang","year":"2020","unstructured":"Wang, B., Dong, M., Ren, M., Wu, Z., Guo, C., Zhuang, T., Pischler, O., Xie, J.: Automatic fault diagnosis of infrared insulator images based on image instance segmentation and temperature analysis. IEEE Trans. Instrum. Meas. 69(8), 5345\u20135355 (2020). https:\/\/doi.org\/10.1109\/TIM.2020.2965635","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"1390_CR3","doi-asserted-by":"publisher","first-page":"66387","DOI":"10.1109\/ACCESS.2021.3076410","volume":"9","author":"SF Stefenon","year":"2021","unstructured":"Stefenon, S.F., Ribeiro, M.H.D.M., Nied, A., Mariani, V.C., Coelho, L.D.S., Leithardt, V.R.Q., Silva, L.A., Seman, L.O.: Hybrid wavelet stacking ensemble model for insulators contamination forecasting. IEEE Access 9, 66387\u201366397 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3076410","journal-title":"IEEE Access"},{"issue":"3","key":"1390_CR4","doi-asserted-by":"publisher","first-page":"924","DOI":"10.1109\/TDEI.2019.008523","volume":"27","author":"H Wang","year":"2020","unstructured":"Wang, H., Cheng, L., Liao, R., Zhang, S., Yang, L.: Nonlinear ultrasonic nondestructive detection and modelling of kissing defects in high voltage composite insulators. IEEE Trans. Dielectr. Electr. Insul. 27(3), 924\u2013931 (2020). https:\/\/doi.org\/10.1109\/TDEI.2019.008523","journal-title":"IEEE Trans. Dielectr. Electr. Insul."},{"key":"1390_CR5","doi-asserted-by":"publisher","first-page":"33980","DOI":"10.1109\/ACCESS.2022.3161506","volume":"10","author":"SF Stefenon","year":"2022","unstructured":"Stefenon, S.F., Bruns, R., Sartori, A., Meyer, L.H., Ovejero, R.G., Leithardt, V.R.Q.: Analysis of the ultrasonic signal in polymeric contaminated insulators through ensemble learning methods. IEEE Access 10, 33980\u201333991 (2022). https:\/\/doi.org\/10.1109\/ACCESS.2022.3161506","journal-title":"IEEE Access"},{"key":"1390_CR6","doi-asserted-by":"publisher","first-page":"61797","DOI":"10.1109\/ACCESS.2019.2915985","volume":"7","author":"H Jiang","year":"2019","unstructured":"Jiang, H., Qiu, X., Chen, J., Liu, X., Miao, X., Zhuang, S.: Insulator fault detection in aerial images based on ensemble learning with multi-level perception. IEEE Access 7, 61797\u201361810 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2915985","journal-title":"IEEE Access"},{"key":"1390_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijepes.2020.105862","volume":"118","author":"AB Alhassan","year":"2020","unstructured":"Alhassan, A.B., Zhang, X., Shen, H., Xu, H.: Power transmission line inspection robots: a review, trends and challenges for future research. Int. J. Electr. Power Energy Syst. 118, 105862 (2020). https:\/\/doi.org\/10.1016\/j.ijepes.2020.105862","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"1390_CR8","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1016\/j.ijepes.2019.03.050","volume":"110","author":"M Samadi","year":"2019","unstructured":"Samadi, M., Seifi, H., Haghifam, M.-R.: Midterm system level maintenance scheduling of transmission equipment using inspection based model. Int. J. Electr. Power Energy Syst. 110, 467\u2013476 (2019). https:\/\/doi.org\/10.1016\/j.ijepes.2019.03.050","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"1390_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijepes.2021.107336","volume":"134","author":"SF Stefenon","year":"2022","unstructured":"Stefenon, S.F., Seman, L.O., Sopelsa Neto, N.F., Meyer, L.H., Nied, A., Yow, K.-C.: Echo state network applied for classification of medium voltage insulators. Int. J. Electr. Power Energy Syst. 134, 107336 (2022). https:\/\/doi.org\/10.1016\/j.ijepes.2021.107336","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"1390_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijepes.2020.106726","volume":"128","author":"H Manninen","year":"2021","unstructured":"Manninen, H., Ramlal, C.J., Singh, A., Rocke, S., Kilter, J., Landsberg, M.: Toward automatic condition assessment of high-voltage transmission infrastructure using deep learning techniques. Int. J. Electr. Power Energy Syst. 128, 106726 (2021). https:\/\/doi.org\/10.1016\/j.ijepes.2020.106726","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"1390_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijepes.2021.107102","volume":"133","author":"SR Fahim","year":"2021","unstructured":"Fahim, S.R., Sarker, S.K., Muyeen, S.M., Das, S.K., Kamwa, I.: A deep learning based intelligent approach in detection and classification of transmission line faults. Int. J. Electrical Power Energy Syst. 133, 107102 (2021). https:\/\/doi.org\/10.1016\/j.ijepes.2021.107102","journal-title":"Int. J. Electrical Power Energy Syst."},{"key":"1390_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijepes.2020.106484","volume":"125","author":"Y Wang","year":"2021","unstructured":"Wang, Y., Yan, J., Yang, Z., Zhao, Y., Liu, T.: Optimizing GIS partial discharge pattern recognition in the ubiquitous power internet of things context: a MixNet deep learning model. Int. J. Electr. Power Energy Syst. 125, 106484 (2021). https:\/\/doi.org\/10.1016\/j.ijepes.2020.106484","journal-title":"Int. J. Electr. Power Energy Syst."},{"issue":"21","key":"1390_CR13","doi-asserted-by":"publisher","first-page":"8323","DOI":"10.3390\/s22218323","volume":"22","author":"NW Branco","year":"2022","unstructured":"Branco, N.W., Cavalca, M.S.M., Stefenon, S.F., Leithardt, V.R.Q.: Wavelet LSTM for fault forecasting in electrical power grids. Sensors 22(21), 8323 (2022). https:\/\/doi.org\/10.3390\/s22218323","journal-title":"Sensors"},{"issue":"13","key":"1390_CR14","doi-asserted-by":"publisher","first-page":"4859","DOI":"10.3390\/s22134859","volume":"22","author":"SF Stefenon","year":"2022","unstructured":"Stefenon, S.F., Singh, G., Yow, K.-C., Cimatti, A.: Semi-ProtoPNet deep neural network for the classification of defective power grid distribution structures. Sensors 22(13), 4859 (2022). https:\/\/doi.org\/10.3390\/s22134859","journal-title":"Sensors"},{"key":"1390_CR15","doi-asserted-by":"publisher","first-page":"15796","DOI":"10.1109\/ACCESS.2021.3051411","volume":"9","author":"H Teimourzadeh","year":"2021","unstructured":"Teimourzadeh, H., Moradzadeh, A., Shoaran, M., Mohammadi-Ivatloo, B., Razzaghi, R.: High impedance single-phase faults diagnosis in transmission lines via deep reinforcement learning of transfer functions. IEEE Access 9, 15796\u201315809 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3051411","journal-title":"IEEE Access"},{"issue":"6","key":"1390_CR16","doi-asserted-by":"publisher","first-page":"1070","DOI":"10.35833\/MPCE.2020.000190","volume":"8","author":"G Luo","year":"2020","unstructured":"Luo, G., Hei, J., Yao, C., He, J., Li, M.: An end-to-end transient recognition method for VSC-HVDC based on deep belief network. J. Mod. Power Syst. Clean Energy 8(6), 1070\u20131079 (2020). https:\/\/doi.org\/10.35833\/MPCE.2020.000190","journal-title":"J. Mod. Power Syst. Clean Energy"},{"key":"1390_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijepes.2020.106726","volume":"128","author":"H Manninen","year":"2021","unstructured":"Manninen, H., Ramlal, C.J., Singh, A., Rocke, S., Kilter, J., Landsberg, M.: Toward automatic condition assessment of high-voltage transmission infrastructure using deep learning techniques. Int. J. Electr. Power Energy Syst. 128, 106726 (2021). https:\/\/doi.org\/10.1016\/j.ijepes.2020.106726","journal-title":"Int. J. Electr. Power Energy Syst."},{"issue":"6","key":"1390_CR18","doi-asserted-by":"publisher","first-page":"7208","DOI":"10.1109\/TIA.2020.3017698","volume":"56","author":"S Wang","year":"2020","unstructured":"Wang, S., Dehghanian, P.: On the use of artificial intelligence for high impedance fault detection and electrical safety. IEEE Trans. Ind. Appl. 56(6), 7208\u20137216 (2020). https:\/\/doi.org\/10.1109\/TIA.2020.3017698","journal-title":"IEEE Trans. Ind. Appl."},{"issue":"3","key":"1390_CR19","doi-asserted-by":"publisher","first-page":"2331","DOI":"10.1109\/TSG.2020.3041853","volume":"12","author":"M Dabbaghjamanesh","year":"2021","unstructured":"Dabbaghjamanesh, M., Moeini, A., Hatziargyriou, N.D., Zhang, J.: Deep learning-based real-time switching of hybrid AC\/DC transmission networks. IEEE Trans. Smart Grid 12(3), 2331\u20132342 (2021). https:\/\/doi.org\/10.1109\/TSG.2020.3041853","journal-title":"IEEE Trans. Smart Grid"},{"issue":"9","key":"1390_CR20","doi-asserted-by":"publisher","first-page":"1732","DOI":"10.3390\/diagnostics11091732","volume":"11","author":"G Singh","year":"2021","unstructured":"Singh, G., Yow, K.-C.: Object or background: an interpretable deep learning model for Covid-19 detection from CT-scan images. Diagnostics 11(9), 1732 (2021). https:\/\/doi.org\/10.3390\/diagnostics11091732","journal-title":"Diagnostics"},{"issue":"6","key":"1390_CR21","doi-asserted-by":"publisher","first-page":"1096","DOI":"10.1049\/gtd2.12353","volume":"16","author":"SF Stefenon","year":"2021","unstructured":"Stefenon, S.F., Corso, M.P., Nied, A., Perez, F.L., Yow, K.-C., Gonzalez, G.V., Leithardt, V.R.Q.: Classification of insulators using neural network based on computer vision. IET Gener. Transm. Distrib. 16(6), 1096\u20131107 (2021). https:\/\/doi.org\/10.1049\/gtd2.12353","journal-title":"IET Gener. Transm. Distrib."},{"key":"1390_CR22","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1016\/j.ijepes.2019.05.060","volume":"113","author":"PHV Rocha","year":"2019","unstructured":"Rocha, P.H.V., Costa, E.G., Serres, A.R., Xavier, G.V.R., Peixoto, J.E.B., Lins, R.L.: Inspection in overhead insulators through the analysis of the irradiated RF spectrum. Int. J. Electr. Power Energy Syst. 113, 355\u2013361 (2019). https:\/\/doi.org\/10.1016\/j.ijepes.2019.05.060","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"1390_CR23","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.ijepes.2017.12.016","volume":"99","author":"VN Nguyen","year":"2018","unstructured":"Nguyen, V.N., Jenssen, R., Roverso, D.: Automatic autonomous vision-based power line inspection: a review of current status and the potential role of deep learning. Int. J. Electr. Power Energy Syst. 99, 107\u2013120 (2018). https:\/\/doi.org\/10.1016\/j.ijepes.2017.12.016","journal-title":"Int. J. Electr. Power Energy Syst."},{"issue":"1","key":"1390_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1177\/1729881417752821","volume":"15","author":"X Hui","year":"2018","unstructured":"Hui, X., Bian, J., Zhao, X., Tan, M.: Vision-based autonomous navigation approach for unmanned aerial vehicle transmission-line inspection. Int. J. Adv. Rob. Syst. 15(1), 1\u201315 (2018). https:\/\/doi.org\/10.1177\/1729881417752821","journal-title":"Int. J. Adv. Rob. Syst."},{"issue":"9","key":"1390_CR25","doi-asserted-by":"publisher","first-page":"6080","DOI":"10.1109\/TIM.2020.2969057","volume":"69","author":"Z Zhao","year":"2020","unstructured":"Zhao, Z., Qi, H., Qi, Y., Zhang, K., Zhai, Y., Zhao, W.: Detection method based on automatic visual shape clustering for pin-missing defect in transmission lines. IEEE Trans. Instrum. Meas. 69(9), 6080\u20136091 (2020). https:\/\/doi.org\/10.1109\/TIM.2020.2969057","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"1","key":"1390_CR26","doi-asserted-by":"publisher","first-page":"165","DOI":"10.3390\/app9010165","volume":"9","author":"O Men\u00e9ndez","year":"2019","unstructured":"Men\u00e9ndez, O., P\u00e9rez, M., Auat Cheein, F.: Visual-based positioning of aerial maintenance platforms on overhead transmission lines. Appl. Sci. 9(1), 165 (2019). https:\/\/doi.org\/10.3390\/app9010165","journal-title":"Appl. Sci."},{"key":"1390_CR27","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1016\/j.isatra.2019.11.007","volume":"100","author":"MF da Silva","year":"2020","unstructured":"da Silva, M.F., Hon\u00f3rio, L.M., Marcato, A.L.M., Vidal, V.F., Santos, M.F.: Unmanned aerial vehicle for transmission line inspection using an extended Kalman filter with colored electromagnetic interference. ISA Trans. 100, 322\u2013333 (2020). https:\/\/doi.org\/10.1016\/j.isatra.2019.11.007","journal-title":"ISA Trans."},{"key":"1390_CR28","doi-asserted-by":"publisher","first-page":"4557","DOI":"10.1007\/s00202-022-01641-1","volume":"104","author":"SF Stefenon","year":"2022","unstructured":"Stefenon, S.F., Yow, K.-C., Nied, A., Meyer, L.H.: Classification of distribution power grid structures using inception v3 deep neural network. Electr. Eng. 104, 4557\u20134569 (2022). https:\/\/doi.org\/10.1007\/s00202-022-01641-1","journal-title":"Electr. Eng."},{"key":"1390_CR29","doi-asserted-by":"publisher","first-page":"38448","DOI":"10.1109\/ACCESS.2020.2974798","volume":"8","author":"H Liang","year":"2020","unstructured":"Liang, H., Zuo, C., Wei, W.: Detection and evaluation method of transmission line defects based on deep learning. IEEE Access 8, 38448\u201338458 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.2974798","journal-title":"IEEE Access"},{"key":"1390_CR30","doi-asserted-by":"publisher","first-page":"9945","DOI":"10.1109\/ACCESS.2019.2891123","volume":"7","author":"X Miao","year":"2019","unstructured":"Miao, X., Liu, X., Chen, J., Zhuang, S., Fan, J., Jiang, H.: Insulator detection in aerial images for transmission line inspection using single shot multibox detector. IEEE Access 7, 9945\u20139956 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2891123","journal-title":"IEEE Access"},{"key":"1390_CR31","doi-asserted-by":"publisher","first-page":"94065","DOI":"10.1109\/ACCESS.2020.2995608","volume":"8","author":"J Zhu","year":"2020","unstructured":"Zhu, J., Guo, Y., Yue, F., Yuan, H., Yang, A., Wang, X., Rong, M.: A deep learning method to detect foreign objects for inspecting power transmission lines. IEEE Access 8, 94065\u201394075 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.2995608","journal-title":"IEEE Access"},{"key":"1390_CR32","doi-asserted-by":"publisher","first-page":"97830","DOI":"10.1109\/ACCESS.2020.2995910","volume":"8","author":"Y Guo","year":"2020","unstructured":"Guo, Y., Pang, Z., Du, J., Jiang, F., Hu, Q.: An improved AlexNet for power edge transmission line anomaly detection. IEEE Access 8, 97830\u201397838 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.2995910","journal-title":"IEEE Access"},{"issue":"4","key":"1390_CR33","doi-asserted-by":"publisher","first-page":"633","DOI":"10.1109\/JSTSP.2018.2849593","volume":"12","author":"K Maeda","year":"2018","unstructured":"Maeda, K., Takahashi, S., Ogawa, T., Haseyama, M.: Estimation of deterioration levels of transmission towers via deep learning maximizing canonical correlation between heterogeneous features. IEEE J. Sel. Top. Signal Process. 12(4), 633\u2013644 (2018). https:\/\/doi.org\/10.1109\/JSTSP.2018.2849593","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"1390_CR34","doi-asserted-by":"publisher","first-page":"184841","DOI":"10.1109\/ACCESS.2020.3029857","volume":"8","author":"S Wang","year":"2020","unstructured":"Wang, S., Liu, Y., Qing, Y., Wang, C., Lan, T., Yao, R.: Detection of insulator defects with improved ResNeSt and region proposal network. IEEE Access 8, 184841\u2013184850 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3029857","journal-title":"IEEE Access"},{"issue":"5","key":"1390_CR35","doi-asserted-by":"publisher","first-page":"531","DOI":"10.3390\/rs11050531","volume":"11","author":"Y Wang","year":"2019","unstructured":"Wang, Y., Wang, C., Zhang, H., Dong, Y., Wei, S.: Automatic ship detection based on RetinaNet using multi-resolution Gaofen-3 imagery. Remote Sens. 11(5), 531 (2019). https:\/\/doi.org\/10.3390\/rs11050531","journal-title":"Remote Sens."},{"key":"1390_CR36","doi-asserted-by":"publisher","first-page":"2430","DOI":"10.1016\/j.egyr.2020.09.002","volume":"6","author":"J Liu","year":"2020","unstructured":"Liu, J., Jia, R., Li, W., Ma, F., Abdullah, H.M., Ma, H., Mohamed, M.A.: High precision detection algorithm based on improved RetinaNet for defect recognition of transmission lines. Energy Rep. 6, 2430\u20132440 (2020). https:\/\/doi.org\/10.1016\/j.egyr.2020.09.002","journal-title":"Energy Rep."},{"key":"1390_CR37","doi-asserted-by":"publisher","first-page":"182105","DOI":"10.1109\/ACCESS.2020.3027850","volume":"8","author":"P Zhang","year":"2020","unstructured":"Zhang, P., Zhang, Z., Hao, Y., Zhou, Z., Luo, B., Wang, T.: Multi-scale feature enhanced domain adaptive object detection for power transmission line inspection. IEEE Access 8, 182105\u2013182116 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3027850","journal-title":"IEEE Access"},{"key":"1390_CR38","doi-asserted-by":"publisher","first-page":"149999","DOI":"10.1109\/ACCESS.2020.3016213","volume":"8","author":"S Kim","year":"2020","unstructured":"Kim, S., Kim, D., Jeong, S., Ham, J.-W., Lee, J.-K., Oh, K.-Y.: Fault diagnosis of power transmission lines using a UAV-mounted smart inspection system. IEEE Access 8, 149999\u2013150009 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3016213","journal-title":"IEEE Access"},{"issue":"10","key":"1390_CR39","doi-asserted-by":"publisher","first-page":"953","DOI":"10.1049\/iet-smt.2020.0083","volume":"14","author":"SF Stefenon","year":"2020","unstructured":"Stefenon, S.F., Freire, R.Z., Meyer, L.H., Corso, M.P., Sartori, A., Nied, A., Klaar, A.C.R., Yow, K.-C.: Fault detection in insulators based on ultrasonic signal processing using a hybrid deep learning technique. IET Sci. Meas. Technol. 14(10), 953\u2013961 (2020). https:\/\/doi.org\/10.1049\/iet-smt.2020.0083","journal-title":"IET Sci. Meas. Technol."},{"issue":"4","key":"1390_CR40","doi-asserted-by":"publisher","first-page":"1486","DOI":"10.1109\/TSMC.2018.2871750","volume":"50","author":"X Tao","year":"2020","unstructured":"Tao, X., Zhang, D., Wang, Z., Liu, X., Zhang, H., Xu, D.: Detection of power line insulator defects using aerial images analyzed with convolutional neural networks. IEEE Trans. Syst. Man Cybern. Syst. 50(4), 1486\u20131498 (2020). https:\/\/doi.org\/10.1109\/TSMC.2018.2871750","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"1390_CR41","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijepes.2021.106987","volume":"130","author":"H Guan","year":"2021","unstructured":"Guan, H., Sun, X., Su, Y., Hu, T., Wang, H., Wang, H., Peng, C., Guo, Q.: UAV-lidar aids automatic intelligent powerline inspection. Int. J. Electr. Power Energy Syst. 130, 106987 (2021). https:\/\/doi.org\/10.1016\/j.ijepes.2021.106987","journal-title":"Int. J. Electr. Power Energy Syst."},{"issue":"1","key":"1390_CR42","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1007\/s00202-020-01099-z","volume":"103","author":"T Lin","year":"2021","unstructured":"Lin, T., Liu, X.: An intelligent recognition system for insulator string defects based on dimension correction and optimized faster R-CNN. Electr. Eng. 103(1), 541\u2013549 (2021). https:\/\/doi.org\/10.1007\/s00202-020-01099-z","journal-title":"Electr. Eng."},{"key":"1390_CR43","unstructured":"Farhadi, A., Redmon, J.: Yolov3: an incremental improvement. In: Computer Vision and Pattern Recognition, pp. 1804\u201302767. Springer, Berlin (2018)"},{"issue":"2","key":"1390_CR44","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","volume":"42","author":"T-Y Lin","year":"2020","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. IEEE Trans. Pattern Anal. Mach. Intell. 42(2), 318\u2013327 (2020). https:\/\/doi.org\/10.1109\/TPAMI.2018.2858826","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1390_CR45","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117\u20132125 (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"1390_CR46","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 770\u2013778. IEEE, Las Vegas (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"1390_CR47","doi-asserted-by":"publisher","unstructured":"Redmon, J., Farhadi, A.: Yolo9000: better, faster, stronger. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6517\u20136525 (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.690","DOI":"10.1109\/CVPR.2017.690"},{"key":"1390_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijepes.2023.108982","volume":"148","author":"BJ Souza","year":"2023","unstructured":"Souza, B.J., Stefenon, S.F., Singh, G., Freire, R.Z.: Hybrid-yolo for classification of insulators defects in transmission lines based on UAV. Int. J. Electr. Power Energy Syst. 148, 108982 (2023). https:\/\/doi.org\/10.1016\/j.ijepes.2023.108982","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"1390_CR49","unstructured":"Ultralytics, G.: YOLOv8 in PyTorch. https:\/\/github.com\/ultralytics\/ultralytics (2023)"},{"key":"1390_CR50","doi-asserted-by":"publisher","unstructured":"Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., Wei, X.: YOLOv6: a single-stage object detection framework for industrial applications. arXiv (2022). https:\/\/doi.org\/10.48550\/ARXIV.2209.02976","DOI":"10.48550\/ARXIV.2209.02976"},{"key":"1390_CR51","doi-asserted-by":"publisher","unstructured":"Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv (2022). https:\/\/doi.org\/10.48550\/ARXIV.2207.02696","DOI":"10.48550\/ARXIV.2207.02696"},{"key":"1390_CR52","unstructured":"Ultralytics, G.: YOLOv5 in PyTorch. https:\/\/github.com\/ultralytics\/yolov5 (2022)"},{"key":"1390_CR53","unstructured":"Chen, C., Li, O., Tao, C., Barnett, A.J., Su, J., Rudin, C.: This looks like that: deep learning for interpretable image recognition. arXiv preprint arXiv:1806.105745, 1\u201312 (2018)"},{"key":"1390_CR54","doi-asserted-by":"publisher","first-page":"85198","DOI":"10.1109\/ACCESS.2021.3087583","volume":"9","author":"G Singh","year":"2021","unstructured":"Singh, G., Yow, K.-C.: An interpretable deep learning model for Covid-19 detection with chest X-ray images. IEEE Access 9, 85198\u201385208 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3087583","journal-title":"IEEE Access"},{"key":"1390_CR55","doi-asserted-by":"publisher","first-page":"41482","DOI":"10.1109\/ACCESS.2021.3064838","volume":"9","author":"G Singh","year":"2021","unstructured":"Singh, G., Yow, K.-C.: These do not look like those: an interpretable deep learning model for image recognition. IEEE Access 9, 41482\u201341493 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3064838","journal-title":"IEEE Access"},{"issue":"3","key":"1390_CR56","first-page":"1","volume":"1341","author":"D Erhan","year":"2009","unstructured":"Erhan, D., Bengio, Y., Courville, A., Vincent, P.: Visualizing higher-layer features of a deep network. Univ. Montreal 1341(3), 1\u201313 (2009)","journal-title":"Univ. Montreal"},{"key":"1390_CR57","doi-asserted-by":"publisher","first-page":"599","DOI":"10.1007\/978-3-642-35289-8_32","volume-title":"A Practical Guide to Training Restricted Boltzmann Machines","author":"GE Hinton","year":"2012","unstructured":"Hinton, G.E.: A Practical Guide to Training Restricted Boltzmann Machines, pp. 599\u2013619. Springer, Berlin (2012). https:\/\/doi.org\/10.1007\/978-3-642-35289-8_32"},{"key":"1390_CR58","doi-asserted-by":"publisher","unstructured":"Lee, H., Grosse, R., Ranganath, R., Ng, A.Y.: Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In: Proceedings of the 26th Annual International Conference on Machine Learning. ICML09, pp. 609\u2013616. Association for Computing Machinery, New York (2009). https:\/\/doi.org\/10.1145\/1553374.1553453","DOI":"10.1145\/1553374.1553453"},{"key":"1390_CR59","doi-asserted-by":"publisher","unstructured":"Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Computer Vision (ECCV 2014), pp. 818\u2013833. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10590-1_53","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"1390_CR60","unstructured":"Simonyan, K., Vedaldi, A., Zisserman, A.: Deep inside convolutional networks: visualising image classification models and saliency maps. In: Workshop at International Conference on Learning Representations, pp. 1\u20138 (2014)"},{"key":"1390_CR61","doi-asserted-by":"publisher","unstructured":"Sundararajan, M., Taly, A., Yan, Q.: Axiomatic attribution for deep networks. arXiv (2017). https:\/\/doi.org\/10.48550\/ARXIV.1703.01365","DOI":"10.48550\/ARXIV.1703.01365"},{"key":"1390_CR62","unstructured":"Smilkov, D., Thorat, N., Kim, B., Vi\u00e9gas, F., Wattenberg, M.: Smoothgrad: removing noise by adding noise. arXiv preprint arXiv:1706.038251, 1\u201310 (2017)"},{"key":"1390_CR63","doi-asserted-by":"publisher","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: visual explanations from deep networks via gradient-based localization. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 618\u2013626. IEEE, Venice (2017). https:\/\/doi.org\/10.1109\/ICCV.2017.74","DOI":"10.1109\/ICCV.2017.74"},{"key":"1390_CR64","doi-asserted-by":"publisher","unstructured":"Zheng, H., Fu, J., Mei, T., Luo, J.: Learning multi-attention convolutional neural network for fine-grained image recognition. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 5219\u20135227. IEEE, Venice (2017). https:\/\/doi.org\/10.1109\/ICCV.2017.557","DOI":"10.1109\/ICCV.2017.557"},{"key":"1390_CR65","doi-asserted-by":"publisher","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2921\u20132929. IEEE, Las Vegas (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.319","DOI":"10.1109\/CVPR.2016.319"},{"key":"1390_CR66","doi-asserted-by":"publisher","unstructured":"Zhang, N., Donahue, J., Girshick, R., Darrell, T.: Part-based R-CNNs for fine-grained category detection. In: Computer Vision (ECCV2014), pp. 834\u2013849. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10590-1_54","DOI":"10.1007\/978-3-319-10590-1_54"},{"key":"1390_CR67","doi-asserted-by":"crossref","unstructured":"Li, O., Liu, H., Chen, C., Rudin, C.: Deep learning for case-based reasoning through prototypes: A neural network that explains its predictions. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32, pp. 3530\u20133537 (2018)","DOI":"10.1609\/aaai.v32i1.11771"},{"key":"1390_CR68","doi-asserted-by":"publisher","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255. IEEE, Miami (2009). https:\/\/doi.org\/10.1109\/CVPR.2009.5206848","DOI":"10.1109\/CVPR.2009.5206848"},{"issue":"7","key":"1390_CR69","doi-asserted-by":"publisher","first-page":"4847","DOI":"10.1109\/TWC.2020.2987990","volume":"19","author":"KNRSV Prasad","year":"2020","unstructured":"Prasad, K.N.R.S.V., D\u2019souza, K.B., Bhargava, V.K.: A downscaled faster-RCNN framework for signal detection and time-frequency localization in wideband RF systems. IEEE Trans. Wirel. Commun. 19(7), 4847\u20134862 (2020). https:\/\/doi.org\/10.1109\/TWC.2020.2987990","journal-title":"IEEE Trans. Wirel. Commun."},{"issue":"5","key":"1390_CR70","doi-asserted-by":"publisher","first-page":"7853","DOI":"10.1007\/s11042-020-09914-2","volume":"80","author":"GSB Jahangeer","year":"2021","unstructured":"Jahangeer, G.S.B., Rajkumar, T.D.: Early detection of breast cancer using hybrid of series network and VGG-16. Multimed. Tools Appl. 80(5), 7853\u20137886 (2021). https:\/\/doi.org\/10.1007\/s11042-020-09914-2","journal-title":"Multimed. Tools Appl."},{"key":"1390_CR71","doi-asserted-by":"publisher","DOI":"10.1016\/j.gltp.2021.08.027","author":"S Murali","year":"2021","unstructured":"Murali, S., Deepu, R., Shivamurthy, R., et al.: ResNet-50 vs VGG-19 vs training from scratch: a comparative analysis of the segmentation and classification of pneumonia from chest X-ray images. Glob. Transit. Proc. (2021). https:\/\/doi.org\/10.1016\/j.gltp.2021.08.027","journal-title":"Glob. Transit. Proc."},{"issue":"2","key":"1390_CR72","doi-asserted-by":"publisher","first-page":"212","DOI":"10.3390\/f12020212","volume":"12","author":"M Gao","year":"2021","unstructured":"Gao, M., Qi, D., Mu, H., Chen, J.: A transfer residual neural network based on ResNet-34 for detection of wood knot defects. Forests 12(2), 212 (2021). https:\/\/doi.org\/10.3390\/f12020212","journal-title":"Forests"},{"issue":"9","key":"1390_CR73","doi-asserted-by":"publisher","first-page":"212","DOI":"10.3390\/brainsci9090212","volume":"9","author":"LV Fulton","year":"2019","unstructured":"Fulton, L.V., Dolezel, D., Harrop, J., Yan, Y., Fulton, C.P.: Classification of Alzheimer\u2019s disease with and without imagery using gradient boosted machines and ResNet-50. Brain Sci. 9(9), 212 (2019). https:\/\/doi.org\/10.3390\/brainsci9090212","journal-title":"Brain Sci."},{"key":"1390_CR74","doi-asserted-by":"publisher","unstructured":"Pan, T.-S., Huang, H.-C., Lee, J.-C., Chen, C.-H.: Multi-scale ResNet for real-time underwater object detection. In: Signal, Image and Video Processing, pp. 1\u20139 (2020). https:\/\/doi.org\/10.1007\/s11760-020-01818-w","DOI":"10.1007\/s11760-020-01818-w"},{"issue":"3","key":"1390_CR75","doi-asserted-by":"publisher","first-page":"2627","DOI":"10.1007\/s11063-019-10043-7","volume":"50","author":"K Ghiasi-Shirazi","year":"2019","unstructured":"Ghiasi-Shirazi, K.: Generalizing the convolution operator in convolutional neural networks. Neural Process. Lett. 50(3), 2627\u20132646 (2019). https:\/\/doi.org\/10.1007\/s11063-019-10043-7","journal-title":"Neural Process. Lett."},{"key":"1390_CR76","doi-asserted-by":"publisher","unstructured":"Nalaie, K., Ghiasi-Shirazi, K., Akbarzadeh-T, M.-R.: Efficient implementation of a generalized convolutional neural networks based on weighted euclidean distance. In: 2017 7th International Conference on Computer and Knowledge Engineering (ICCKE), pp. 211\u2013216. IEEE, Mashhad (2017). https:\/\/doi.org\/10.1109\/ICCKE.2017.8167877","DOI":"10.1109\/ICCKE.2017.8167877"},{"issue":"23","key":"1390_CR77","doi-asserted-by":"publisher","first-page":"5667","DOI":"10.1049\/iet-gtd.2020.0814","volume":"14","author":"SF Stefenon","year":"2020","unstructured":"Stefenon, S.F., Kasburg, C., Nied, A., Klaar, A.C.R., Ferreira, F.C.S., Branco, N.W.: Hybrid deep learning for power generation forecasting in active solar trackers. IET Gener. Transm. Distrib. 14(23), 5667\u20135674 (2020). https:\/\/doi.org\/10.1049\/iet-gtd.2020.0814","journal-title":"IET Gener. Transm. Distrib."},{"key":"1390_CR78","doi-asserted-by":"publisher","first-page":"157818","DOI":"10.1109\/ACCESS.2019.2950053","volume":"7","author":"H Chen","year":"2019","unstructured":"Chen, H., He, Z., Shi, B., Zhong, T.: Research on recognition method of electrical components based on yolo v3. IEEE Access 7, 157818\u2013157829 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2950053","journal-title":"IEEE Access"},{"key":"1390_CR79","doi-asserted-by":"publisher","unstructured":"Chen, Z., Xiao, Y., Zhou, Y., Li, Z., Liu, Y.: Insulator recognition method for distribution network overhead transmission lines based on modified yolov3. In: 2020 Chinese Automation Congress (CAC), pp. 2815\u20132820 (2020). https:\/\/doi.org\/10.1109\/CAC51589.2020.9327352","DOI":"10.1109\/CAC51589.2020.9327352"},{"key":"1390_CR80","doi-asserted-by":"publisher","unstructured":"Feng, Z., Guo, L., Huang, D., Li, R.: Electrical insulator defects detection method based on yolov5. In: 2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS), Suzhou, China, pp. 979\u2013984 (2021). https:\/\/doi.org\/10.1109\/DDCLS52934.2021.9455519","DOI":"10.1109\/DDCLS52934.2021.9455519"},{"key":"1390_CR81","doi-asserted-by":"publisher","first-page":"80829","DOI":"10.1109\/ACCESS.2019.2923024","volume":"7","author":"L Wang","year":"2019","unstructured":"Wang, L., Chen, Z., Hua, D., Zheng, Z.: Semantic segmentation of transmission lines and their accessories based on UAV-taken images. IEEE Access 7, 80829\u201380839 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2923024","journal-title":"IEEE Access"},{"key":"1390_CR82","doi-asserted-by":"publisher","first-page":"59934","DOI":"10.1109\/ACCESS.2020.2982288","volume":"8","author":"X Li","year":"2020","unstructured":"Li, X., Su, H., Liu, G.: Insulator defect recognition based on global detection and local segmentation. IEEE Access 8, 59934\u201359946 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.2982288","journal-title":"IEEE Access"},{"issue":"4","key":"1390_CR83","doi-asserted-by":"publisher","first-page":"1033","DOI":"10.3390\/s21041033","volume":"21","author":"Q Wen","year":"2021","unstructured":"Wen, Q., Luo, Z., Chen, R., Yang, Y., Li, G.: Deep learning approaches on defect detection in high resolution aerial images of insulators. Sensors 21(4), 1033 (2021). https:\/\/doi.org\/10.3390\/s21041033","journal-title":"Sensors"},{"key":"1390_CR84","doi-asserted-by":"publisher","first-page":"101283","DOI":"10.1109\/ACCESS.2019.2931144","volume":"7","author":"C Sampedro","year":"2019","unstructured":"Sampedro, C., Rodriguez-Vazquez, J., Rodriguez-Ramos, A., Carrio, A., Campoy, P.: Deep learning-based system for automatic recognition and diagnosis of electrical insulator strings. IEEE Access 7, 101283\u2013101308 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2931144","journal-title":"IEEE Access"},{"key":"1390_CR85","doi-asserted-by":"publisher","unstructured":"Vigneshwaran, B., Maheswari, R.V., Kalaivani, L., Shanmuganathan, V., Rho, S., Kadry, S., Lee, M.Y.: Recognition of pollution layer location in 11 kv polymer insulators used in smart power grid using dual-input VGG convolutional neural network. Energy Rep. (2021). https:\/\/doi.org\/10.1016\/j.egyr.2020.12.044","DOI":"10.1016\/j.egyr.2020.12.044"},{"key":"1390_CR86","doi-asserted-by":"publisher","first-page":"96380","DOI":"10.1109\/ACCESS.2021.3095382","volume":"9","author":"C Deng","year":"2021","unstructured":"Deng, C.: The method of insulator defect recognition based on group theory. IEEE Access 9, 96380\u201396389 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3095382","journal-title":"IEEE Access"},{"issue":"5","key":"1390_CR87","doi-asserted-by":"publisher","first-page":"1426","DOI":"10.3390\/en14051426","volume":"14","author":"C Liu","year":"2021","unstructured":"Liu, C., Wu, Y., Liu, J., Han, J.: MTI-YOLO: a light-weight and real-time deep neural network for insulator detection in complex aerial images. Energies 14(5), 1426 (2021). https:\/\/doi.org\/10.3390\/en14051426","journal-title":"Energies"}],"container-title":["Machine Vision and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-023-01390-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00138-023-01390-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-023-01390-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,11]],"date-time":"2023-05-11T06:04:16Z","timestamp":1683785056000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00138-023-01390-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,5]]},"references-count":87,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,5]]}},"alternative-id":["1390"],"URL":"https:\/\/doi.org\/10.1007\/s00138-023-01390-6","relation":{},"ISSN":["0932-8092","1432-1769"],"issn-type":[{"value":"0932-8092","type":"print"},{"value":"1432-1769","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,5]]},"assertion":[{"value":"26 May 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 February 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 March 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 April 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"41"}}